ArticleGenetic epidemiology2020
Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies.
Article in Genetic epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large-scale multi-omics analyses in Hispanic/Latino populations identify genes for cardiometabolic traits.Nature communications · 2025Pooled it
- Article
- Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new schizophrenia-associated genes.Nature genetics · 2026Article
- Co-expression-wide association studies link genetically regulated interactions with complex traits.Nature communications · 2025Article
- Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups.Genetic epidemiology · 2025Article
- Integrating Gene Expression Data into Single-Step Method (ssBLUP) Improves Genomic Prediction Accuracy for Complex Traits of Duroc × Erhualian FCurrent issues in molecular biology · 2024Article
- Article
- A bootstrap model comparison test for identifying genes with context-specific patterns of genetic regulation.The annals of applied statistics · 2024Article
- A BOOTSTRAP MODEL COMPARISON TEST FOR IDENTIFYING GENES WITH CONTEXT-SPECIFIC PATTERNS OF GENETIC REGULATION.bioRxiv : the preprint server for biology · 2023Article
- Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations.HGG advances · 2023Article
- Transcriptome-wide association studies: recent advances in methods, applications and available databases.Communications biology · 2023Review
- Using GWAS summary data to impute traits for genotyped individuals.HGG advances · 2023Article
- Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations.bioRxiv : the preprint server for biology · 2023Article
- Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits.Genetic epidemiology · 2022Article
- Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits.American journal of human genetics · 2022Article
- A flexible summary statistics-based colocalization method with application to the mucin cystic fibrosis lung disease modifier locus.American journal of human genetics · 2022Article
- Cross-tissue transcriptome-wide association studies identify susceptibility genes shared between schizophrenia and inflammatory bowel disease.Communications biology · 2022Article
- Protein prediction for trait mapping in diverse populations.PloS one · 2022Article
- InTACT: An adaptive and powerful framework for joint-tissue transcriptome-wide association studies.Genetic epidemiology · 2021Article
- Transcriptome prediction performance across machine learning models and diverse ancestries.HGG advances · 2021Article
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3 authors.
Funding
Abstract
In transcriptome-wide association studies (TWAS), gene expression values are predicted using genotype data and tested for association with a phenotype. The power of this approach to detect associations relies, at least in part, on the accuracy of the prediction. Here we compare the prediction accuracy of six different methods-LASSO, Ridge regression, Elastic net, Best Linear Unbiased Predictor, Bayesian Sparse Linear Mixed Model, and Random Forests-by performing cross-validation using data from the Geuvadis Project. We also examine prediction accuracy (a) at different sample sizes, (b) when ancestry of the prediction model training and testing populations is different, and (c) when the tissue used to train the model is different from the tissue to be predicted. We find that, for most genes, the expression cannot be accurately predicted, but in general sparse statistical models tend to outperform polygenic models at prediction. Average prediction accuracy is reduced when the model training set size is reduced or when predicting across ancestries and is marginally reduced when predicting across tissues. We conclude that using sparse statistical models and the development of large reference panels across multiple ethnicities and tissues will lead to better prediction of gene expression, and thus may improve TWAS power.
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